The big data challenge - and how polypharmacology supports the translation from pre-clinical research into clinical use against neurodegenerative diseases and beyond

Mobocertinib antagonizes multidrug resistance in ABCB1- and ABCG2-overexpressing cancer cells: In vitro and in vivo studies

Overexpression of ATP-binding cassette (ABC) transporters, particularly ABCB1 and ABCG2, strongly correlates with multidrug resistance (MDR), rendering cancer chemotherapy ineffective. Exploration and identification of novel inhibitors targeting ABCB1 and ABCG2 are necessary to overcome the related MDR. Mobocertinib is an approved EGFR/HER2 inhibitor for non-small cell lung cancer (NSCLC) with EGFR exon 20 insertion mutations. This study demonstrates that mobocertinib can potentially reverse ABCB1- and ABCG2-mediated MDR. Our findings indicate a strong interaction between mobocertinib and these two proteins, supported by its high binding affinity with ABCB1 and ABCG2 models. Through inhibiting the drug efflux function of ABCB1 and ABCG2, mobocertinib facilitates substrate drugs accumulation, thereby re-sensitizing substrate drugs in drug-resistant cancer cells. Additionally, mobocertinib inhibited the ATPase activity of ABCB1 and ABCG2 without changing the expression levels or subcellular localization. In the tumor-bearing mouse model, mobocertinib boosted the antitumor effect of paclitaxel and topotecan, resulting in tumor regression. In summary, our study uncovers a novel potential for repurposing mobocertinib as a dual inhibitor of ABCB1 and ABCG2, and suggests the combination of mobocertinib with substrate drugs as a strategy to counteract MDR.

Predictive, integrative, and regulatory aspects of AI-driven computational toxicology - Highlights of the German Pharm-Tox Summit (GPTS) 2024

The 9th German Pharm-Tox Summit (GPTS) and the 90th Annual Meeting of the German Society for Experimental and Clinical Pharmacology and Toxicology (DGPT) took place in Munich from March 13-15, 2024. The event brought together over 700 participants from around the world to discuss cutting-edge developments in the fields of pharmacology and toxicology as well as scientific innovations and novel insights. A key focus of the conference was on the rapidly increasing role of computational toxicology, artificial intelligence (AI), and machine learning (ML) into the field, marking a shift away from traditional methods and allowing the reduction of animal testing as primary tool for toxicological risk assessment. Tools such as Toxometris.ai showcased the potential of AI-based risk assessments for predicting carcinogenicity, offering more ethical and efficient alternatives. Additionally, computer-driven models like computer-aided pattern analysis (C@PA) for drug toxicity prediction were presented, emphasizing the growing role of chem- and bioinformatic applications in computational sciences. Throughout the summit, there was a strong focus on the need for regulatory innovation to support the adoption of these advanced technologies and ensure the safety and sustainability of chemical substances and drugs.

Polypharmacology prediction: the long road toward comprehensively anticipating small-molecule selectivity to de-risk drug discovery

INTRODUCTION

Small molecules often bind to multiple targets, a behavior termed polypharmacology. Anticipating polypharmacology is essential for drug discovery since unknown off-targets can modulate safety and efficacy - profoundly affecting drug discovery success. Unfortunately, experimental methods to assess selectivity present significant limitations and drugs still fail in the clinic due to unanticipated off-targets. Computational methods are a cost-effective, complementary approach to predict polypharmacology.

AREAS COVERED

This review aims to provide a comprehensive overview of the state of polypharmacology prediction and discuss its strengths and limitations, covering both classical cheminformatics methods and bioinformatic approaches. The authors review available data sources, paying close attention to their different coverage. The authors then discuss major algorithms grouped by the types of data that they exploit using selected examples.

EXPERT OPINION

Polypharmacology prediction has made impressive progress over the last decades and contributed to identify many off-targets. However, data incompleteness currently limits most approaches to comprehensively predict selectivity. Moreover, our limited agreement on model assessment challenges the identification of the best algorithms - which at present show modest performance in prospective real-world applications. Despite these limitations, the exponential increase of multidisciplinary Big Data and AI hold much potential to better polypharmacology prediction and de-risk drug discovery.

Medicinal polypharmacology-a scientific glossary of terminology and concepts

Medicinal polypharmacology is one answer to the complex reality of multifactorial human diseases that are often unresponsive to single-targeted treatment. It is an admittance that intrinsic feedback mechanisms, crosstalk, and disease networks necessitate drugs with broad modes-of-action and multitarget affinities. Medicinal polypharmacology grew to be an independent research field within the last two decades and stretches from basic drug development to clinical research. It has developed its own terminology embedded in general terms of pharmaceutical drug discovery and development at the intersection of medicinal chemistry, chemical biology, and clinical pharmacology. A clear and precise language of critical terms and a thorough understanding of underlying concepts is imperative; however, no comprehensive work exists to this date that could support researchers in this and adjacent research fields. In order to explore novel options, establish interdisciplinary collaborations, and generate high-quality research outputs, the present work provides a first-in-field glossary to clarify the numerous terms that have originated from various individual disciplines.

Medicinal Polypharmacology in the Clinic - Translating the Polypharmacolome into Therapeutic Benefit

Drugs with multiple targets, often annotated as 'unselective', 'promiscuous', 'multitarget', or 'polypharmacological', are widely considered in both academic and industrial research as a high risk due to the likelihood of adverse effects. However, retrospective analyses have shown that particularly approved drugs bear rich polypharmacological profiles. This raises the question whether our perception of the specificity paradigm ('one drug-one target concept') is correct - and if specifically multitarget drugs should be developed instead of being rejected. These questions provoke a paradigm shift - regarding the development of polypharmacological drugs not as a 'waste of investment', but acknowledging the existence of a 'lack of investment'. This perspective provides an insight into modern drug development highlighting latest drug candidates that have not been assessed in a broader polypharmacology-based context elsewhere embedded in a historic framework of classical and modern approved multitarget drugs. The article shall be an inspiration to the scientific community to re-consider current standards, and more, to evolve to a better understanding of polypharmacology from a challenge to an opportunity.

Medicinal polypharmacology: Exploration and exploitation of the polypharmacolome in modern drug development

At the core of complex and multifactorial human diseases, such as cancer, metabolic syndrome, or neurodegeneration, are multiple players that cross-talk in robust biological networks which are intrinsically resilient to alterations. These multifactorial diseases are characterized by sophisticated feedback mechanisms which manifest cellular imbalance and resistance to drug therapy. By adhering to the specificity paradigm ("one target-one drug concept"), research focused for many years on drugs with very narrow mechanisms of action. This narrow focus promoted therapy ineffectiveness and resistance. However, modern drug discovery has evolved over the last years, increasingly emphasizing integral strategies for the development of clinically effective drugs. These integral strategies include the controlled engagement of multiple targets to overcome therapy resistance. Apart from the additive or even synergistic effects in therapy, multitarget drugs harbor molecular-structural attributes to explore orphan targets of which intrinsic substrates/physiological role(s) and/or modulators are unknown for future therapy purposes. We designated this multidisciplinary and translational research field between medicinal chemistry, chemical biology, and molecular pharmacology as 'medicinal polypharmacology'. Medicinal polypharmacology emerged as alternative approach to common single-targeted pharmacology stretching from basic drug and target identification processes to clinical evaluation of multitarget drugs, and the exploration and exploitation of the 'polypharmacolome' is at the forefront of modern drug development research.

HD_BPMDS: a curated binary pattern multitarget dataset of Huntington's disease-targeting agents

The discovery of both distinctive lead molecules and novel drug targets is a great challenge in drug discovery, which particularly accounts for orphan diseases. Huntington's disease (HD) is an orphan, neurodegenerative disease of which the pathology is well-described. However, its pathophysiological background and molecular mechanisms are poorly understood. To date, only 2 drugs have been approved on the US and European markets, both of which address symptomatic aspects of this disease only. Although several hundreds of agents were described with efficacy against the HD phenotype in in vitro and/or in vivo models, a successful translation into clinical use is rarely achieved. Two major impediments are, first, the lack of awareness and understanding of the interactome-the sum of key proteins, cascades, and mediators-that contributes to HD initiation and progression; and second, the translation of the little gained knowledge into useful model systems. To counteract this lack of data awareness, we manually compiled and curated the entire modulator landscape of successfully evaluated pre-clinical small-molecule HD-targeting agents which are annotated with substructural molecular patterns, physicochemical properties, as well as drug targets, and which were linked to benchmark databases such as PubChem, ChEMBL, or UniProt. Particularly, the annotation with substructural molecular patterns expressed as binary code allowed for the generation of target-specific and -unspecific fingerprints which could be used to determine the (poly)pharmacological profile of molecular-structurally distinct molecules.